

'''simple docstring'''





from dataclasses import dataclass
from typing import Optional

import torch
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .modeling_utils import ModelMixin


@dataclass
class  UpperCAmelCase (							UpperCAmelCase__    ):

		'''simple docstring'''






		SCREAMING_SNAKE_CASE_           =					42


class  UpperCAmelCase (							UpperCAmelCase__  , UpperCAmelCase__    ):

		'''simple docstring'''






		@register_to_config
		def __init__(					self   ,			SCREAMING_SNAKE_CASE_ = 16   ,			SCREAMING_SNAKE_CASE_ = 88   ,			SCREAMING_SNAKE_CASE_ = None   ,			SCREAMING_SNAKE_CASE_ = None   ,			SCREAMING_SNAKE_CASE_ = 1   ,			SCREAMING_SNAKE_CASE_ = 0.0   ,			SCREAMING_SNAKE_CASE_ = 32   ,			SCREAMING_SNAKE_CASE_ = None   ,			SCREAMING_SNAKE_CASE_ = False   ,			SCREAMING_SNAKE_CASE_ = None   ,			SCREAMING_SNAKE_CASE_ = "geglu"   ,			SCREAMING_SNAKE_CASE_ = True   ,			SCREAMING_SNAKE_CASE_ = True   ,			)  ->     Tuple:






							'''simple docstring'''



							super().__init__()
							lowerCamelCase_  				=      num_attention_heads
							lowerCamelCase_  				=      attention_head_dim
							lowerCamelCase_  				=      num_attention_heads * attention_head_dim

							lowerCamelCase_  				=      in_channels

							lowerCamelCase_  				=      torch.nn.GroupNorm(num_groups=SCREAMING_SNAKE_CASE_   ,			num_channels=SCREAMING_SNAKE_CASE_   ,			eps=1E-6   ,			affine=SCREAMING_SNAKE_CASE_		)
							lowerCamelCase_  				=      nn.Linear(SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_		)

							# 3. Define transformers blocks
							lowerCamelCase_  				=      nn.ModuleList(
							    [
							        BasicTransformerBlock(
							            SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_   ,			dropout=SCREAMING_SNAKE_CASE_   ,			cross_attention_dim=SCREAMING_SNAKE_CASE_   ,			activation_fn=SCREAMING_SNAKE_CASE_   ,			attention_bias=SCREAMING_SNAKE_CASE_   ,			double_self_attention=SCREAMING_SNAKE_CASE_   ,			norm_elementwise_affine=SCREAMING_SNAKE_CASE_   ,			)
							        for d in range(SCREAMING_SNAKE_CASE_		)
							    ]		)

							lowerCamelCase_  				=      nn.Linear(SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_		)






		def 			UpperCamelCase(					self   ,			SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_=None   ,			SCREAMING_SNAKE_CASE_=None   ,			SCREAMING_SNAKE_CASE_=None   ,			SCREAMING_SNAKE_CASE_=1   ,			SCREAMING_SNAKE_CASE_=None   ,			SCREAMING_SNAKE_CASE_ = True   ,			)  ->     Any:






							'''simple docstring'''



							lowerCamelCase_				,lowerCamelCase_				,lowerCamelCase_				,lowerCamelCase_  				=      hidden_states.shape
							lowerCamelCase_  				=      batch_frames // num_frames

							lowerCamelCase_  				=      hidden_states

							lowerCamelCase_  				=      hidden_states[None, :].reshape(SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_		)
							lowerCamelCase_  				=      hidden_states.permute(0   ,			2   ,			1   ,			3   ,			4		)

							lowerCamelCase_  				=      self.norm(SCREAMING_SNAKE_CASE_		)
							lowerCamelCase_  				=      hidden_states.permute(0   ,			3   ,			4   ,			2   ,			1		).reshape(batch_size * height * width   ,			SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_		)

							lowerCamelCase_  				=      self.proj_in(SCREAMING_SNAKE_CASE_		)

							# 2. Blocks
							for block in self.transformer_blocks:
												lowerCamelCase_  				=      block(
												    SCREAMING_SNAKE_CASE_   ,			encoder_hidden_states=SCREAMING_SNAKE_CASE_   ,			timestep=SCREAMING_SNAKE_CASE_   ,			cross_attention_kwargs=SCREAMING_SNAKE_CASE_   ,			class_labels=SCREAMING_SNAKE_CASE_   ,			)

							# 3. Output
							lowerCamelCase_  				=      self.proj_out(SCREAMING_SNAKE_CASE_		)
							lowerCamelCase_  				=      (
							    hidden_states[None, None, :]
							    .reshape(SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_		)
							    .permute(0   ,			3   ,			4   ,			1   ,			2		)
							    .contiguous()
							)
							lowerCamelCase_  				=      hidden_states.reshape(SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_		)

							lowerCamelCase_  				=      hidden_states + residual

							if not return_dict:
												return (output,)

							return TransformerTemporalModelOutput(sample=SCREAMING_SNAKE_CASE_		)




import numpy as np
import torch
import tqdm

from ...models.unet_ad import UNetaDModel
from ...pipelines import DiffusionPipeline
from ...utils import randn_tensor
from ...utils.dummy_pt_objects import DDPMScheduler




class 					_UpperCAmelCase			(     lowerCAmelCase  ):



 '''simple docstring'''




 def __init__(		self				:   int							, lowercase_				:   UNetaDModel							, lowercase_				:   UNetaDModel							, lowercase_				:   DDPMScheduler							, lowercase_				:   Any							, )						->	Any:


   """simple docstring"""







   super().__init__()
   _UpperCamelCase           =    value_function
   _UpperCamelCase           =    unet
   _UpperCamelCase           =    scheduler
   _UpperCamelCase           =    env
   _UpperCamelCase           =    env.get_dataset()
   _UpperCamelCase           =    {}
   for key in self.data.keys():
     try:
       _UpperCamelCase           =    self.data[key].mean()
     except:  # noqa: E722
       pass
   _UpperCamelCase           =    {}
   for key in self.data.keys():
     try:
       _UpperCamelCase           =    self.data[key].std()
     except:  # noqa: E722
       pass
   _UpperCamelCase           =    env.observation_space.shape[0]
   _UpperCamelCase           =    env.action_space.shape[0]







 def 		__UpperCAmelCase				(		self				:   Any							, lowercase_				:   Optional[int]							, lowercase_				:   Optional[Any])						->	Tuple:


   """simple docstring"""







   return (x_in - self.means[key]) / self.stds[key]







 def 		__UpperCAmelCase				(		self				:   Any							, lowercase_				:   Tuple							, lowercase_				:   Optional[Any])						->	List[Any]:


   """simple docstring"""







   return x_in * self.stds[key] + self.means[key]







 def 		__UpperCAmelCase				(		self				:   List[Any]							, lowercase_				:   Optional[Any])						->	List[Any]:


   """simple docstring"""







   if type(lowercase_) is dict:
     return {k: self.to_torch(lowercase_) for k, v in x_in.items()}
   elif torch.is_tensor(lowercase_):
     return x_in.to(self.unet.device)
   return torch.tensor(lowercase_							, device=self.unet.device)







 def 		__UpperCAmelCase				(		self				:   Optional[Any]							, lowercase_				:   Any							, lowercase_				:   Union[str, Any]							, lowercase_				:   List[str])						->	Tuple:


   """simple docstring"""







   for key, val in cond.items():
     _UpperCamelCase           =    val.clone()
   return x_in







 def 		__UpperCAmelCase				(		self				:   Union[str, Any]							, lowercase_				:   Optional[int]							, lowercase_				:   str							, lowercase_				:   List[Any]							, lowercase_				:   int)						->	Dict:


   """simple docstring"""







   _UpperCamelCase           =    x.shape[0]
   _UpperCamelCase           =    None
   for i in tqdm.tqdm(self.scheduler.timesteps):
     # create batch of timesteps to pass into model
     _UpperCamelCase           =    torch.full((batch_size,)							, lowercase_							, device=self.unet.device							, dtype=torch.long)
     for _ in range(lowercase_):
       with torch.enable_grad():
         x.requires_grad_()

         # permute to match dimension for pre-trained models
         _UpperCamelCase           =    self.value_function(x.permute(0							, 2							, 1)							, lowercase_).sample
         _UpperCamelCase           =    torch.autograd.grad([y.sum()]							, [x])[0]

         _UpperCamelCase           =    self.scheduler._get_variance(lowercase_)
         _UpperCamelCase           =    torch.exp(0.5 * posterior_variance)
         _UpperCamelCase           =    model_std * grad

       _UpperCamelCase           =    0
       _UpperCamelCase           =    x.detach()
       _UpperCamelCase           =    x + scale * grad
       _UpperCamelCase           =    self.reset_xa(lowercase_							, lowercase_							, self.action_dim)

     _UpperCamelCase           =    self.unet(x.permute(0							, 2							, 1)							, lowercase_).sample.permute(0							, 2							, 1)

     # TODO: verify deprecation of this kwarg
     _UpperCamelCase           =    self.scheduler.step(lowercase_							, lowercase_							, lowercase_							, predict_epsilon=lowercase_)["prev_sample"]

     # apply conditions to the trajectory (set the initial state)
     _UpperCamelCase           =    self.reset_xa(lowercase_							, lowercase_							, self.action_dim)
     _UpperCamelCase           =    self.to_torch(lowercase_)
   return x, y







 def __call__(		self				:   Optional[int]							, lowercase_				:   str							, lowercase_				:   int=64							, lowercase_				:   Any=32							, lowercase_				:   List[Any]=2							, lowercase_				:   str=0.1)						->	Optional[int]:


   """simple docstring"""







   _UpperCamelCase           =    self.normalize(lowercase_							, "observations")
   _UpperCamelCase           =    obs[None].repeat(lowercase_							, axis=0)

   _UpperCamelCase           =    {0: self.to_torch(lowercase_)}
   _UpperCamelCase           =    (batch_size, planning_horizon, self.state_dim + self.action_dim)

   # generate initial noise and apply our conditions (to make the trajectories start at current state)
   _UpperCamelCase           =    randn_tensor(lowercase_							, device=self.unet.device)
   _UpperCamelCase           =    self.reset_xa(lowercase_							, lowercase_							, self.action_dim)
   _UpperCamelCase           =    self.to_torch(lowercase_)

   # run the diffusion process
   _UpperCamelCase      ,						_UpperCamelCase           =    self.run_diffusion(lowercase_							, lowercase_							, lowercase_							, lowercase_)

   # sort output trajectories by value
   _UpperCamelCase           =    y.argsort(0							, descending=lowercase_).squeeze()
   _UpperCamelCase           =    x[sorted_idx]
   _UpperCamelCase           =    sorted_values[:, :, : self.action_dim]
   _UpperCamelCase           =    actions.detach().cpu().numpy()
   _UpperCamelCase           =    self.de_normalize(lowercase_							, key="actions")

   # select the action with the highest value
   if y is not None:
     _UpperCamelCase           =    0
   else:
     # if we didn't run value guiding, select a random action
     _UpperCamelCase           =    np.random.randint(0							, lowercase_)

   _UpperCamelCase           =    denorm_actions[selected_index, 0]
   return denorm_actions




